arXiv subjects

Christian Bressen Pipper

Publications and source records attributed to Christian Bressen Pipper.

6 recordsLinked to original sources

arXiv2026

A framework for joint assessment of a terminal event and a score existing only in the absence of the terminal event

Analysis of data from randomized controlled trials in vulnerable populations requires special attention when assessing treatment effect by a score measuring, e.g., disease stage or activity together with onset of prevalent terminal events. In reality, it is impossible to disentangle a disease score from the terminal event, since the score is not clinically meaningful after this event. In this work, we propose to assess treatment interventions simultaneously on the terminal event and the disease score in the absence of a terminal event. Our proposal is based on a natural data-generating mechanism, respecting that a disease score does not exist beyond the terminal event. We use modern semi-parametric statistical methods to provide robust and efficient estimation of the risk of terminal event and expected disease score conditional on no terminal event at a pre-specified landmark time. We also use the simultaneous asymptotic behaviour of our estimators to develop a powerful closed testing procedure for confirmatory assessment of treatment effect on both onset of terminal event and level of disease score in the absence of a terminal event. A simulation study mimicking a large-scale outcome trial in chronic kidney patients as well as an analysis of that trial is provided to assess performance.

stat.ME↗

arXiv2026

A joint QoL-Survival framework with debiased estimation under truncation by death

Evaluating quality-of-life (QoL) outcomes in populations with high mortality risk is complicated by truncation by death, since QoL is undefined for individuals who do not survive to the planned measurement time. We propose a framework that jointly models the distribution of QoL and survival without extrapolating QoL beyond death. Inspired by multistate formulations, we extend the joint characterization of binary health states and mortality to continuous QoL outcomes. Because treatment effects cannot be meaningfully summarized in a single one-dimensional estimand without strong assumptions, our approach simultaneously considers both survival and the joint distribution of QoL and survival with the latter conveniently displayed in a simplex. We develop assumption-lean, semiparametric estimators based on efficient influence functions, yielding flexible, root-n consistent estimators that accommodate machine-learning methods while making transparent the conditions these must satisfy. The proposed method is illustrated through simulation studies and two real-data applications.

stat.ME↗

arXiv2025

A general approach to construct powerful tests for intersections of one-sided null-hypotheses based on influence functions

Testing intersections of null-hypotheses is an integral part of closed testing procedures for assessing multiple null-hypotheses under family-wise type 1 error control. Popular intersection tests such as the minimum p-value test are based on marginal p-values and are typically evaluated conservatively by disregarding simultaneous behavior of the marginal p-values. We consider a general purpose Wald type test for testing intersections of one-sided null-hypotheses. The test is constructed on the basis of the simultaneous asymptotic behavior of the p values. The simultaneous asymptotic behavior is derived via influence functions of estimators using the so-called stacking approach. In particular, this approach does not require added assumptions on simultaneous behavior to be valid. The resulting test is shown to have attractive power properties and thus forms the basis of a powerful closed testing procedure for testing multiple one-sided hypotheses under family-wise type 1 error control.

stat.ME↗

arXiv2025

Causal interpretation of the sibling comparison and its relation to the cross-over design

The intuitive motivation for employing a sibling comparison design is to adjust for confounding that is constant within families. Such confounding can be caused by variables that otherwise might prove difficult to measure, for example factors relating to genetics, environment, and upbringing. Recent methodological investigations have shown that despite its intuitive appeal, the conventionally employed analysis does not relate to a well-defined causal target, even in the case of constant confounding. A main challenge is that the analysis will target the subpopulation of exposure discordant pairs. In the presence of an effect of the cosibling's exposure on the sibling's outcome, there is a second challenge, namely that the effect corresponds to an intervention that always exposes the cosibling to the opposite exposure from the sibling. We characterise the sibling comparison in terms of the cross-over design. Estimands of interest are discussed before using this characterisation to establish more natural conditions for targeting an appropriate causal parameter. We cast the above-mentioned challenges of the sibling comparison in terms of those facing the cross-over trialist: in order to target an appropriate estimand one must be able to argue the absence of a certain type of carry-over effect as well as absence of trial-by-treatment interaction, thus establishing that the former study design emulates the latter warts and all. We explore weighting to counter the effects of such interactions and to target other estimands. The weights rely on estimates of the unobserved confounding structure. Through simulations and an example analysis, we illustrate its potential usefulness to assess the validity of the assumptions of the matched analysis. We briefly discuss an extension of the weighting procedure to remove selection bias based on data from a population-level reference sample.

stat.ME↗

arXiv2024

Estimation methods for estimands using the treatment policy strategy; a simulation study based on the PIONEER 1 Trial

Estimands using the treatment policy strategy for addressing intercurrent events are common in Phase III clinical trials. One estimation approach for this strategy is retrieved dropout whereby observed data following an intercurrent event are used to multiply impute missing data. However, such methods have had issues with variance inflation and model fitting due to data sparsity. This paper introduces likelihood-based versions of these approaches, investigating and comparing their statistical properties to the existing retrieved dropout approaches, simpler analysis models and reference-based multiple imputation. We use a simulation based upon the data from the PIONEER 1 Phase III clinical trial in Type II diabetics to present complex and relevant estimation challenges. The likelihood-based methods display similar statistical properties to their multiple imputation equivalents, but all retrieved dropout approaches suffer from high variance. Retrieved dropout approaches appear less biased than reference-based approaches, resulting in a bias-variance trade-off, but we conclude that the large degree of variance inflation is often more problematic than the bias. Therefore, only the simpler retrieved dropout models appear appropriate as a primary analysis in a clinical trial, and only where it is believed most data following intercurrent events will be observed. The jump-to-reference approach may represent a more promising estimation approach for symptomatic treatments due to its relatively high power and ability to fit in the presence of much missing data, despite its strong assumptions and tendency towards conservative bias. More research is needed to further develop how to estimate the treatment effect for a treatment policy strategy.

stat.AP↗

arXiv2019

Moment-based Estimation of Mixtures of Regression Models

Finite mixtures of regression models provide a flexible modeling framework for many phenomena. Using moment-based estimation of the regression parameters, we develop unbiased estimators with a minimum of assumptions on the mixture components. In particular, only the average regression model for one of the components in the mixture model is needed and no requirements on the distributions. The consistency and asymptotic distribution of the estimators is derived and the proposed method is validated through a series of simulation studies and is shown to be highly accurate. We illustrate the use of the moment-based mixture of regression models with an application to wine quality data.

math.ST↗